source('../settings/settings.R')
source('commonFunctions.R')
persons <- SELECTED_SUBJECTS
drive <- 1
inputFile <- str_interp('../data/processed/distancewise/TT1_Drive_${drive}_${distPrev}m_${distNext}m.csv', list(drive=drive, distPrev=DISTANCE_PREV, distNext=DISTANCE_NEXT))
outputFile <- str_interp("../data/processed/analysis/TT1_Drive_${drive}_PP_${distPrev}m_${distNext}m.csv", list(drive=drive, distPrev=DISTANCE_PREV, distNext=DISTANCE_NEXT))
all_Drive1 <- read.csv(inputFile)
all_Drive1$Subject <- as.factor(all_Drive1$Subject)
all_Drive1$logPerspiration <- log(all_Drive1$Perspiration)
mean_pp <- vector(mode="list", length=length(persons))
names(mean_pp) <- persons
std_pp <- vector(mode="list", length=length(persons))
names(std_pp) <- persons
# Segments
mean_pp_seg0 <- vector(mode="list", length=length(persons))
names(mean_pp_seg0) <- persons
mean_pp_seg1 <- vector(mode="list", length=length(persons))
names(mean_pp_seg1) <- persons
mean_pp_seg2 <- vector(mode="list", length=length(persons))
names(mean_pp_seg2) <- persons
mean_pp_seg3 <- vector(mode="list", length=length(persons))
names(mean_pp_seg3) <- persons
mean_pp_seg4 <- vector(mode="list", length=length(persons))
names(mean_pp_seg4) <- persons
for(p in persons) {
pData <- all_Drive1[(all_Drive1$Subject==as.integer(p) | all_Drive1$Subject==p),]
pData_act1 <- pData[pData$Activity == 1,]
if(p == "41") {
# This subjest has suspecious PP in the last phase (Phase = 4)
pData_seg0 <- pData[pData$Phase==0 & pData$Time <= 370,]
} else {
pData_seg0 <- pData[pData$Phase==0,]
}
pData_seg1 <- pData[pData$Phase==1 & pData$Activity==1 & pData$Time < 110,]
pData_seg2 <- pData[pData$Phase==2 & pData$Activity==1 & pData$Time < 250,]
pData_seg3 <- pData[pData$Phase==3 & pData$Activity==1 & pData$Time < 350,]
pData_seg4 <- pData[pData$Phase==4 & pData$Activity==1,]
if(p == "41") {
# This subjest has suspecious PP in the last phase (Phase = 4)
mean_pp[[p]] <- mean(pData_act1[pData_act1$Time <= 370,]$ppLogNormalized)
} else {
mean_pp[[p]] <- mean(pData_act1$ppLogNormalized)
}
std_pp[[p]] <- sd(pData$ppLogNormalized)
mean_pp_seg0[[p]] <- mean(pData_seg0$ppLogNormalized)
mean_pp_seg1[[p]] <- mean(pData_seg1$ppLogNormalized)
mean_pp_seg2[[p]] <- mean(pData_seg2$ppLogNormalized)
mean_pp_seg3[[p]] <- mean(pData_seg3$ppLogNormalized)
if (p == "41") {
mean_pp_seg4[[p]] <- mean_pp[[p]]
} else {
mean_pp_seg4[[p]] <- mean(pData_seg4$ppLogNormalized)
}
}
plt_AllAcc <- vector(mode="list", length=length(persons))
names(plt_AllAcc) <- persons
COLOR_ACC = "#02A3C8"
COLOR_PP = "#F28E8E"
COLOR_BRAKE = "#888888"
y1 <- list(
tickfont = list(color = COLOR_ACC),
title="Degree",
range=c(0, max(all_Drive1$Acceleration))
)
y2 <- list(
tickfont = list(color = COLOR_PP),
overlaying = "y",
side = "right",
title = "Log Perspiration",
showgrid = FALSE,
range=c(-max(all_Drive1$ppLogNormalized), max(all_Drive1$ppLogNormalized))
)
for (p in persons) {
pData <- all_Drive1[all_Drive1$Subject==as.integer(p) | all_Drive1$Subject==p,]
pData_seg0 <- pData[pData$Phase==0,]
pData_seg1 <- pData[pData$Phase==1 & pData$Activity==1 & pData$Time < 110,]
pData_seg2 <- pData[pData$Phase==2 & pData$Activity==1 & pData$Time < 250,]
pData_seg3 <- pData[pData$Phase==3 & pData$Activity==1 & pData$Time < 350,]
pData_seg4 <- pData[pData$Phase==4 & pData$Activity==1,]
plot_Acc <- plot_ly(pData, x = ~Time, height=400, width=900) %>%
# add_trace(name="Acceleration", y = ~Acceleration, type = 'scatter', mode = 'lines', line=list(width=1.5, color=COLOR_ACC)) %>%
add_trace(name="PP", y = ~ppLogNormalized, type = 'scatter', mode = 'lines', connectgaps=F, line=list(width=1.5, color=COLOR_PP), yaxis = "y2") %>%
add_segments(x = min(pData$Time), xend = max(pData$Time), y = mean_pp[[p]], yend = mean_pp[[p]],
yaxis = "y2", name="Avg. PP (straight)",
line=list(color="darkgray", dash = 'dot')) %>%
add_segments(x = min(pData$Time), xend = max(pData$Time), y = mean_pp_seg0[[p]], yend = mean_pp_seg0[[p]],
yaxis = "y2", name="Avg. PP (turning)",
line=list(color="black", dash = 'dot')) %>%
add_segments(x = min(pData_seg1$Time), xend = max(pData_seg1$Time), y = mean_pp_seg1[[p]], yend = mean_pp_seg1[[p]],
yaxis = "y2", name="Avg. PP (1st part)",
line=list(color="red", dash = 'dot')) %>%
add_segments(x = min(pData_seg2$Time), xend = max(pData_seg2$Time), y = mean_pp_seg2[[p]], yend = mean_pp_seg2[[p]],
yaxis = "y2", name="Avg. PP (2nd part)",
line=list(color="green", dash = 'dot')) %>%
add_segments(x = min(pData_seg3$Time), xend = max(pData_seg3$Time), y = mean_pp_seg3[[p]], yend = mean_pp_seg3[[p]],
yaxis = "y2", name="Avg. PP (3rd part)",
line=list(color="blue", dash = 'dot')) %>%
add_segments(x = min(pData_seg4$Time), xend = max(pData_seg4$Time), y = mean_pp_seg4[[p]], yend = mean_pp_seg4[[p]],
yaxis = "y2", name="Avg. PP (4th part)",
line=list(color="purple", dash = 'dot')) %>%
layout(
title=paste0("Subject #", p),
xaxis=list(title="Time [s]", range=c(0)),
yaxis=y1,
yaxis2=y2,
margin = list(l = 50, r = 50, b = 50, t = 50, pad = 4),
legend = list(x = 0.5, xanchor = "center", y = -0.4, bgcolor = "rgba(0,0,0,0)", title="Metric", orientation = "h"),
autosize = F
)
plt_AllAcc[[p]] <- plot_Acc
}
no non-missing arguments to min; returning Infno non-missing arguments to max; returning -Inf
htmltools::tagList(plt_AllAcc)
NUMBER_OF_CLUSTERS = 3
color_darkpink = "#e75480"
CLUSTER_BRANCH_COLORS <- c("blue", "darkred", color_darkpink, "black")[1:NUMBER_OF_CLUSTERS]
CLUSTER_LABEL_COLORS <- c("blue", "darkred", color_darkpink, "black")[1:NUMBER_OF_CLUSTERS]
dfPP <- as.data.frame(cbind(
unlist(mean_pp),
unlist(std_pp),
unlist(mean_pp_seg0),
unlist(mean_pp_seg1),
unlist(mean_pp_seg2),
unlist(mean_pp_seg3),
unlist(mean_pp_seg4)))
names(dfPP) <- c("MeanPP", "StdPP", "MeanPP_Seg0", "MeanPP_Seg1", "MeanPP_Seg2", "MeanPP_Seg3", "MeanPP_Seg4")
behavioralMatrixClustering <- as.matrix(dfPP)
distMatrix <- dist(behavioralMatrixClustering)
hresults <- distMatrix %>% hclust
hc <- hresults %>%
as.dendrogram %>%
set("nodes_cex", NUMBER_OF_CLUSTERS) %>%
set("labels_col", value = CLUSTER_LABEL_COLORS, k=NUMBER_OF_CLUSTERS) %>%
# set("leaves_pch", 19) %>%
# set("leaves_col", value = c("gray"), k=NUMBER_OF_CLUSTERS) %>%
set("branches_k_color", value=CLUSTER_BRANCH_COLORS, k=NUMBER_OF_CLUSTERS)
plot(hc)
legend("topright",
title="Drive=Cognitive \nHierachical Clustering",
legend = c("Group 1", "Group 2", "Group 3"),
col = c("darkred", "pink" , "blue"),
pch = c(20,20,20), bty = "n", pt.cex = 1.5, cex = 0.8 ,
text.col = "black", horiz = FALSE, inset = c(0.4, 0.1))

# Store analysis data
dfx <- dfPP
dfx <- cbind(persons, dfx)
names(dfx) <- c("Subject", names(dfPP))
write.csv(dfx, outputFile, row.names = F)
Linear Model
sampledData <- getSampleSegmentedData(NA, all_Drive1, window=WINDOW_TIME)
linearModelOnline <- lmer(ppNext ~
(1 | Subject)
+ Speed_u
+ Speed_std
+ Acc_u
+ Acc_std
+ Brake_u
+ Brake_std
+ Steering_u
+ Steering_std,
data=sampledData, REML = T)
# lmer(ppLogNormalized ~ (1 | Subject) + Speed_u + Speed_std + Acc_u + Acc_std + Brake_u + Brake_std + Steering_u + Steering_std + HR + BR, data = pData, REML = T)
# anova(model)
summary(linearModelOnline)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: ppNext ~ (1 | Subject) + Speed_u + Speed_std + Acc_u + Acc_std + Brake_u + Brake_std + Steering_u + Steering_std
Data: sampledData
REML criterion at convergence: -9935.5
Scaled residuals:
Min 1Q Median 3Q Max
-6.7105 -0.5402 -0.0175 0.5512 3.7751
Random effects:
Groups Name Variance Std.Dev.
Subject (Intercept) 0.0002925 0.01710
Residual 0.0026650 0.05162
Number of obs: 3275, groups: Subject, 21
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.224e-01 1.234e-02 1.281e+03 9.920 < 2e-16 ***
Speed_u -5.814e-03 3.798e-04 3.197e+03 -15.308 < 2e-16 ***
Speed_std -3.434e-03 1.520e-03 3.265e+03 -2.259 0.02393 *
Acc_u 2.557e-03 4.012e-04 2.694e+03 6.372 2.18e-10 ***
Acc_std 5.077e-03 3.503e-04 3.265e+03 14.491 < 2e-16 ***
Brake_u 4.210e-03 7.247e-04 3.255e+03 5.810 6.87e-09 ***
Brake_std -3.922e-03 7.216e-04 3.265e+03 -5.434 5.91e-08 ***
Steering_u 4.846e-05 1.176e-05 3.265e+03 4.119 3.89e-05 ***
Steering_std 1.061e-04 3.268e-05 3.264e+03 3.246 0.00118 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) Speed_ Spd_st Acc_u Acc_st Brake_ Brk_st Sterng_
Speed_u -0.819
Speed_std -0.157 0.056
Acc_u -0.268 -0.223 0.040
Acc_std -0.257 0.160 -0.133 0.044
Brake_u -0.168 0.107 -0.068 0.148 0.102
Brake_std -0.046 0.207 -0.094 -0.232 -0.300 -0.835
Steering_u -0.314 0.305 0.069 0.052 0.040 0.342 -0.197
Steerng_std -0.609 0.551 -0.235 0.201 0.434 0.156 -0.164 0.070
plot(linearModelOnline)

behavioralColumns <- BEHAVIORAL_COLUMNS
behavioralMatrix <- matrix(nrow = length(persons), ncol = length(behavioralColumns))
---
title: "R Notebook"
output: html_notebook
---

```{r}
source('../settings/settings.R')
source('commonFunctions.R')
```

```{r}
persons <- SELECTED_SUBJECTS
drive <- 1
inputFile <- str_interp('../data/processed/distancewise/TT1_Drive_${drive}_${distPrev}m_${distNext}m.csv', list(drive=drive, distPrev=DISTANCE_PREV, distNext=DISTANCE_NEXT))
outputFile <- str_interp("../data/processed/analysis/TT1_Drive_${drive}_PP_${distPrev}m_${distNext}m.csv", list(drive=drive, distPrev=DISTANCE_PREV, distNext=DISTANCE_NEXT))

all_Drive1 <- read.csv(inputFile)
all_Drive1$Subject <- as.factor(all_Drive1$Subject)
all_Drive1$logPerspiration <- log(all_Drive1$Perspiration)
```


```{r}
mean_pp <- vector(mode="list", length=length(persons)) 
names(mean_pp) <- persons

std_pp <- vector(mode="list", length=length(persons)) 
names(std_pp) <- persons

# Segments
mean_pp_seg0 <- vector(mode="list", length=length(persons)) 
names(mean_pp_seg0) <- persons
mean_pp_seg1 <- vector(mode="list", length=length(persons)) 
names(mean_pp_seg1) <- persons
mean_pp_seg2 <- vector(mode="list", length=length(persons)) 
names(mean_pp_seg2) <- persons
mean_pp_seg3 <- vector(mode="list", length=length(persons)) 
names(mean_pp_seg3) <- persons
mean_pp_seg4 <- vector(mode="list", length=length(persons)) 
names(mean_pp_seg4) <- persons


for(p in persons) {
  pData <- all_Drive1[(all_Drive1$Subject==as.integer(p) | all_Drive1$Subject==p),]
  pData_act1 <- pData[pData$Activity == 1,]
  
  if(p == "41") {
    # This subjest has suspecious PP in the last phase (Phase = 4)
    pData_seg0 <- pData[pData$Phase==0 & pData$Time <= 370,]
  } else {
    pData_seg0 <- pData[pData$Phase==0,]
  }
  
  pData_seg1 <- pData[pData$Phase==1 & pData$Activity==1 & pData$Time < 110,]
  pData_seg2 <- pData[pData$Phase==2 & pData$Activity==1 & pData$Time < 250,]
  pData_seg3 <- pData[pData$Phase==3 & pData$Activity==1 & pData$Time < 350,]
  pData_seg4 <- pData[pData$Phase==4 & pData$Activity==1,]
  
  if(p == "41") {
    # This subjest has suspecious PP in the last phase (Phase = 4)
    mean_pp[[p]] <- mean(pData_act1[pData_act1$Time <= 370,]$ppLogNormalized)
  } else {
    mean_pp[[p]] <- mean(pData_act1$ppLogNormalized)
  }
  
  std_pp[[p]] <- sd(pData$ppLogNormalized)
  mean_pp_seg0[[p]] <- mean(pData_seg0$ppLogNormalized)
  mean_pp_seg1[[p]] <- mean(pData_seg1$ppLogNormalized)
  mean_pp_seg2[[p]] <- mean(pData_seg2$ppLogNormalized)
  mean_pp_seg3[[p]] <- mean(pData_seg3$ppLogNormalized)
  
  if (p == "41") {
    mean_pp_seg4[[p]] <- mean_pp[[p]]
  } else {
    mean_pp_seg4[[p]] <- mean(pData_seg4$ppLogNormalized)
  }
}

```

```{r}
plt_AllAcc <- vector(mode="list", length=length(persons)) 
names(plt_AllAcc) <- persons

COLOR_ACC = "#02A3C8"
COLOR_PP = "#F28E8E"
COLOR_BRAKE = "#888888"

y1 <- list(
  tickfont = list(color = COLOR_ACC),
  title="Degree",
  range=c(0, max(all_Drive1$Acceleration))
)
y2 <- list(
  tickfont = list(color = COLOR_PP),
  overlaying = "y",
  side = "right",
  title = "Log Perspiration",
  showgrid = FALSE,
  range=c(-max(all_Drive1$ppLogNormalized), max(all_Drive1$ppLogNormalized))
)

for (p in persons) {
  pData <- all_Drive1[all_Drive1$Subject==as.integer(p) | all_Drive1$Subject==p,]
  
  pData_seg0 <- pData[pData$Phase==0,]
  pData_seg1 <- pData[pData$Phase==1 & pData$Activity==1 & pData$Time < 110,]
  pData_seg2 <- pData[pData$Phase==2 & pData$Activity==1 & pData$Time < 250,]
  pData_seg3 <- pData[pData$Phase==3 & pData$Activity==1 & pData$Time < 350,]
  pData_seg4 <- pData[pData$Phase==4 & pData$Activity==1,]
  
  plot_Acc <- plot_ly(pData, x = ~Time, height=400, width=900) %>%
    # add_trace(name="Acceleration", y = ~Acceleration, type = 'scatter', mode = 'lines', line=list(width=1.5, color=COLOR_ACC)) %>% 
    add_trace(name="PP", y = ~ppLogNormalized, type = 'scatter', mode = 'lines', connectgaps=F, line=list(width=1.5, color=COLOR_PP), yaxis = "y2") %>%
    add_segments(x = min(pData$Time), xend = max(pData$Time), y = mean_pp[[p]], yend = mean_pp[[p]],
                           yaxis = "y2", name="Avg. PP (straight)",
                           line=list(color="darkgray", dash = 'dot')) %>%
    add_segments(x = min(pData$Time), xend = max(pData$Time), y = mean_pp_seg0[[p]], yend = mean_pp_seg0[[p]], 
                           yaxis = "y2", name="Avg. PP (turning)",
                           line=list(color="black", dash = 'dot')) %>%
    add_segments(x = min(pData_seg1$Time), xend = max(pData_seg1$Time), y = mean_pp_seg1[[p]], yend = mean_pp_seg1[[p]], 
                           yaxis = "y2", name="Avg. PP (1st part)",
                           line=list(color="red", dash = 'dot')) %>%
    add_segments(x = min(pData_seg2$Time), xend = max(pData_seg2$Time), y = mean_pp_seg2[[p]], yend = mean_pp_seg2[[p]], 
                           yaxis = "y2", name="Avg. PP (2nd part)",
                           line=list(color="green", dash = 'dot')) %>%
    add_segments(x = min(pData_seg3$Time), xend = max(pData_seg3$Time), y = mean_pp_seg3[[p]], yend = mean_pp_seg3[[p]], 
                           yaxis = "y2", name="Avg. PP (3rd part)",
                           line=list(color="blue", dash = 'dot')) %>%
    add_segments(x = min(pData_seg4$Time), xend = max(pData_seg4$Time), y = mean_pp_seg4[[p]], yend = mean_pp_seg4[[p]], 
                           yaxis = "y2", name="Avg. PP (4th part)",
                           line=list(color="purple", dash = 'dot')) %>%
    layout(
      title=paste0("Subject #", p), 
      xaxis=list(title="Time [s]", range=c(0)), 
      yaxis=y1, 
      yaxis2=y2, 
      margin = list(l = 50, r = 50, b = 50, t = 50, pad = 4),
      legend = list(x = 0.5, xanchor = "center", y = -0.4, bgcolor = "rgba(0,0,0,0)", title="Metric", orientation = "h"),
      autosize = F
    )
  
  plt_AllAcc[[p]] <- plot_Acc
}


htmltools::tagList(plt_AllAcc)
```


```{r}
NUMBER_OF_CLUSTERS = 3

color_darkpink = "#e75480"
CLUSTER_BRANCH_COLORS <- c("blue", "darkred", color_darkpink, "black")[1:NUMBER_OF_CLUSTERS]
CLUSTER_LABEL_COLORS <- c("blue", "darkred", color_darkpink, "black")[1:NUMBER_OF_CLUSTERS]


dfPP <- as.data.frame(cbind(
                            unlist(mean_pp), 
                            unlist(std_pp), 
                            unlist(mean_pp_seg0), 
                            unlist(mean_pp_seg1), 
                            unlist(mean_pp_seg2), 
                            unlist(mean_pp_seg3), 
                            unlist(mean_pp_seg4)))

names(dfPP) <- c("MeanPP", "StdPP", "MeanPP_Seg0", "MeanPP_Seg1", "MeanPP_Seg2", "MeanPP_Seg3", "MeanPP_Seg4")
behavioralMatrixClustering <- as.matrix(dfPP)

distMatrix <- dist(behavioralMatrixClustering)
hresults <- distMatrix %>% hclust

hc <- hresults %>% 
      as.dendrogram %>%
      set("nodes_cex", NUMBER_OF_CLUSTERS) %>%
      set("labels_col", value = CLUSTER_LABEL_COLORS, k=NUMBER_OF_CLUSTERS) %>%
      # set("leaves_pch", 19) %>%
      # set("leaves_col", value = c("gray"), k=NUMBER_OF_CLUSTERS) %>%    
      set("branches_k_color", value=CLUSTER_BRANCH_COLORS, k=NUMBER_OF_CLUSTERS)

plot(hc)
legend("topright", 
     title="Drive=Cognitive \nHierachical Clustering",
     legend = c("Group 1", "Group 2", "Group 3"), 
     col = c("darkred", "pink" , "blue"),
     pch = c(20,20,20), bty = "n",  pt.cex = 1.5, cex = 0.8 , 
     text.col = "black", horiz = FALSE, inset = c(0.4, 0.1))
```



```{r}
# Store analysis data
dfx <- dfPP
dfx <- cbind(persons, dfx)
names(dfx) <- c("Subject", names(dfPP))
write.csv(dfx, outputFile, row.names = F)
```

## Linear Model
```{r}
sampledData <- getSampleSegmentedData(NA, all_Drive1, window=WINDOW_TIME)
linearModelOnline <- lmer(ppNext ~ 
              (1 | Subject)
              + Speed_u
              + Speed_std
              + Acc_u
              + Acc_std
              + Brake_u
              + Brake_std
              + Steering_u
              + Steering_std, 
            data=sampledData, REML = T)

# lmer(ppLogNormalized ~ (1 | Subject) + Speed_u + Speed_std + Acc_u + Acc_std + Brake_u + Brake_std + Steering_u + Steering_std + HR + BR, data = pData, REML = T)

# anova(model)
summary(linearModelOnline)
plot(linearModelOnline)
```

```{r}
behavioralColumns <- BEHAVIORAL_COLUMNS
behavioralMatrix <- matrix(nrow = length(persons), ncol = length(behavioralColumns))
```


